Robot Task Control Using Local Distributed Processing
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Solution Overview
Problem
Robots equipped with low processing power face operational challenges due to high computational loads, which are exacerbated by latency and power consumption issues when relying on cloud computing, compromising rapid task execution.
Innovation Solution
A method and system that allows robots to borrow processing power from local devices in their environment by establishing direct communication links, reducing latency and enabling efficient task execution through distributed processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If a robot is equipped with high processing power devices to manage high computational loads, then the robot's processing capability is improved, but the power consumption increases and the robot's mobility is compromised due to larger batteries
Solution Approach 1:
The processing tasks are segmented and distributed between the robot's onboard processing devices and external cloud computing resources. The robot divides computational loads into local execution and remote execution, allowing it to maintain mobility with reduced power consumption while still accessing high processing power when needed through cloud connectivity.
2Power
If a robot uses cloud computing resources to compensate for reduced onboard processing power, then the robot's processing capability is improved, but latency issues arise that compromise rapid task execution
Solution Approach 1:
The system implements local quality by providing different processing capabilities for different task requirements. Critical time-sensitive tasks are executed locally on the robot's onboard devices with low latency, while non-time-critical tasks are offloaded to cloud computing resources. This creates a heterogeneous processing architecture where local and remote resources serve different functional needs.
Solution Approach 2:
The robot dynamically adjusts its processing strategy based on task requirements, environmental conditions, and available resources. It can switch between local execution, edge computing, and cloud computing modes, optimizing the balance between processing power and latency for each specific task scenario.
3Adaptability or versatility
If a robot relies on internet communication with remote servers, then access to cloud computing power is improved, but communication latency and packet loss affect correct operation
Solution Approach 1:
The patent introduces edge computing nodes as intermediaries between the robot and remote cloud servers. These edge nodes provide a local caching and processing layer that reduces dependency on distant internet connections, thereby improving communication reliability and reducing latency while maintaining access to cloud computing resources.
Solution Approach 2:
The system performs preliminary actions by pre-loading data, models, and computational resources to edge computing nodes before they are needed by the robot. This anticipatory approach ensures that when the robot needs processing power, the resources are already available locally or nearby, reducing communication delays and improving reliability.
Data Source
AI summary
A method for controlling the operation of a robot in an environment is provided. The robot is configured to exchange data with a remote server by establishing a robot-remote server communication through a communication network. The method comprises: having the robot be required to perform a task in the environment; generating a robot procedure for executing the task, the robot procedure comprising a corresponding set of procedures each corresponding to one or more software applications comprising instructions for controlling the robot; selecting a set of at least one device, each device having a processing unit configured to execute at least one software application, each device being located in the environment; the remote server causing each device to download and install at least one software applications; having the robot perform the robot procedure at least partially under the control of commands sent by at least one device.